Human neuroanatomy is enormously variable across subjects - a factor that limits the power of brain studies to detect effects of interest. While degeneration in subcortical structures and cortical gray matter is manifest in many conditions such as aging, Alzheimer's disease, Huntington's disease, multiple sclerosis and schizophrenia, large studies are needed in order to find robust and stable effects that separate groups. Furthermore drug development becomes highly costly as detecting small reductions in atrophy can take years and hundreds or thousands of subjects. These factors raise the importance of longitudinal studies, in which one acquires data at multiple time points and examines the differences in temporal trajectories. Compared to a cross-sectional approach, the longitudinal design can provide more sensitivity and specificity for examining subtle associations by reducing the confounding effect of between-subject variability. Moreover, a serial assessment can be the only way to unambiguously characterize the effect of interest in a randomized experiment, such as a drug trial. Finally, longitudinal studies provide unique insights into the temporal dynamics of the underlying biological process, such as disease progression. Taking full advantage of a longitudinal design requires the optimization of the computational tools that perform image processing and hypothesis testing. In this project, we propose to design, develop and distribute intrinsically longitudinal image processing and hypothesis testing tools and validate them in the study of a set of neurodegenerative diseases.